Junior Machine Learning Engineer - Remote Job, 1+ Year Experience

1 Month ago • 1 Years + • Artificial Intelligence • $65,000 PA - $76,000 PA

Job Summary

Job Description

As a Junior Machine Learning Engineer at Patterned Learning, you'll contribute to the development and implementation of machine learning solutions. Responsibilities include collaborating with senior engineers and data scientists, collecting and preprocessing data, implementing and optimizing machine learning models and algorithms, participating in model evaluation and tuning, improving existing infrastructure, staying current with ML advancements, and integrating solutions into production systems. You'll work on exciting projects in a collaborative environment, gaining valuable experience and developing your skills. The role requires strong problem-solving, communication, and collaboration skills.
Must have:
  • Bachelor's/Master's in CS/Data Science
  • Understanding of ML fundamentals
  • Proficiency in Python/Java/C++
  • Experience with ML frameworks (TensorFlow, PyTorch)
  • Data preprocessing & model evaluation
Good to have:
  • Deep learning knowledge
  • Big data processing tools (Hadoop, Spark)

Job Details

This is a remote position.

Junior Machine Learning Engineer - Remote Job, 1+ Year Experience


Annual Income: $65K - $76K


A valid work permit is necessary in the US


About us: Patterned Learning is a platform that aims to help developers code faster and more efficiently. It offers features such as collaborative coding, real-time multiplayer editing, and the ability to build, test, and deploy directly from the browser. The platform also provides tightly integrated code generation, editing, and output capabilities.



Summary:

As a Junior Machine Learning Engineer, you will have the opportunity to work on exciting projects, develop your skills, and contribute to the development and implementation of machine learning solutions. This is an excellent opportunity for individuals looking to kick-start their careers in the field of machine learning and gain valuable experience in a collaborative and supportive environment.

Responsibilities:

  • Collaborate with senior engineers and data scientists to understand project requirements and develop machine learning models and algorithms.

  • Assist in collecting, preprocessing, and analyzing data to uncover patterns and insights.

  • Implement and optimize machine learning models, algorithms, and pipelines.

  • Participate in model evaluation, validation, and performance tuning.

  • Contribute to the development and improvement of existing machine learning infrastructure and frameworks.

  • Stay up-to-date with the latest advancements in machine learning and actively participate in knowledge-sharing activities within the team.

  • Collaborate with cross-functional teams to integrate machine learning solutions into production systems.

  • Document technical processes, methodologies, and outcomes effectively.

Qualifications:

  • Bachelor's or Master's degree in Computer Science, Data Science, Machine Learning, or a related field.

  • Solid understanding of machine learning fundamentals, algorithms, and statistical concepts.

  • Proficiency in programming languages such as Python, Java, or C++.

  • Familiarity with machine learning frameworks and libraries, such as TensorFlow, PyTorch, or scikit-learn.

  • Experience with data preprocessing, feature engineering, and model evaluation techniques.

  • Knowledge of deep learning architectures and techniques is a plus.

  • Familiarity with big data processing tools (e.g., Hadoop, Spark) is advantageous.

  • Strong problem-solving skills and the ability to work on multiple projects simultaneously.

  • Excellent communication and collaboration skills.

  • Self-motivated with a strong desire to learn and grow in the field of machine learning.


Why Patterned Learning LLC?


Patterned Learning can provide intelligent suggestions, automate repetitive tasks, and assist developers in writing code more effectively. This can help reduce coding errors, improve productivity, and accelerate the development process.


The pattern recognition is particularly relevant in the context of coding. Neural networks, especially deep learning models, are commonly employed for pattern detection and classification tasks. These models simulate human decision-making and can identify patterns in data, making them well-suited for tasks like code analysis and generation.



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